TAGS: 3D Tumor-Adaptive Guidance for SAM

• URL: http://arxiv.org/abs/2505.17096v1
• Date: Wed, 21 May 2025 04:02:17 GMT
• Title: TAGS: 3D Tumor-Adaptive Guidance for SAM
• Authors: Sirui Li, Linkai Peng, Zheyuan Zhang, Gorkem Durak, Ulas Bagci,
• Abstract summary: We propose an adaptation framework called TAGS: Tumor Adaptive Guidance for SAM.<n>It unlocks 2D FMs for 3D medical tasks through multi-prompt fusion.<n>Our model surpasses the state-of-the-art medical image segmentation models.
• Score: 4.073510647434655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models (FMs) such as CLIP and SAM have recently shown great promise in image segmentation tasks, yet their adaptation to 3D medical imaging-particularly for pathology detection and segmentation-remains underexplored. A critical challenge arises from the domain gap between natural images and medical volumes: existing FMs, pre-trained on 2D data, struggle to capture 3D anatomical context, limiting their utility in clinical applications like tumor segmentation. To address this, we propose an adaptation framework called TAGS: Tumor Adaptive Guidance for SAM, which unlocks 2D FMs for 3D medical tasks through multi-prompt fusion. By preserving most of the pre-trained weights, our approach enhances SAM's spatial feature extraction using CLIP's semantic insights and anatomy-specific prompts. Extensive experiments on three open-source tumor segmentation datasets prove that our model surpasses the state-of-the-art medical image segmentation models (+46.88% over nnUNet), interactive segmentation frameworks, and other established medical FMs, including SAM-Med2D, SAM-Med3D, SegVol, Universal, 3D-Adapter, and SAM-B (at least +13% over them). This highlights the robustness and adaptability of our proposed framework across diverse medical segmentation tasks.

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